2014 ModelingDelayedFeedbackinDispla

• (Chapelle, 2014) ⇒ Olivier Chapelle. (2014). “Modeling Delayed Feedback in Display Advertising.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623634

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+Modeling+Delayed+Feedback+in+Display+Advertising
• http://dl.acm.org/citation.cfm?id=2623330.2623634&preflayout=flat#citedby

Quotes

Author Keywords

• Conversion prediction; display advertising; machine learning; on-line information services

Abstract

In performance display advertising a key metric of a campaign effectiveness is its conversion rate -- the proportion of users who take a predefined action on the advertiser website, such as a purchase. Predicting this conversion rate is thus essential for estimating the value of an impression and can be achieved via machine learning. One difficulty however is that the conversions can take place long after the impression -- up to a month -- and this delayed feedback hinders the conversion modeling. We tackle this issue by introducing an additional model that captures the conversion delay. Intuitively, this probabilistic model helps determining whether a user that has not converted should be treated as a negative sample -- when the elapsed time is larger than the predicted delay -- or should be discarded from the training set -- when it is too early to tell. We provide experimental results on real traffic logs that demonstrate the effectiveness of the proposed model.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ModelingDelayedFeedbackinDispla	Olivier Chapelle			Modeling Delayed Feedback in Display Advertising				10.1145/2623330.2623634		2014